Learning Natural Language Understanding Systems from Unaligned Labels for Voice Command in Smart Homes

Anastasiia Mishakova (1) , François Portet (1) , Thierry Desot (1) , Michel Vacher (1)

1 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole

Voice command smart home systems have become a target for the industry to provide more natural human computer interaction. To interpret voice command, systems must be able to extract the meaning from natural language; this task is called Natural Language Understanding (NLU). Modern NLU is based on statistical models which are trained on data. However, a current limitation of most NLU statistical models is the dependence on large amount of textual data aligned with target semantic labels. This is highly time-consuming. Moreover, they require training several separate models for predicting intents, slot-labels and slot-values. In this paper, we propose to use a sequence-to-sequence neural architecture to train NLU models which do not need aligned data and can jointly learn the intent, slot-label and slot-value prediction tasks. This approach has been evaluated both on a voice command dataset we acquired for the purpose of the study as well as on a publicly available dataset. The experiments show that a single model learned on unaligned data is competitive with state-of-the-art models which depend on aligned data.

Mots clés: Voice-User Interface; Natural Language Understanding; Smart Environments; Deep Neural Network

Domaines: Intelligence artificielle [cs.AI]

Anastasiia Mishakova, François Portet, Thierry Desot, Michel Vacher. Learning Natural Language Understanding Systems from Unaligned Labels for Voice Command in Smart Homes. The 1st International Workshop on Pervasive Computing and Spoken Dialogue Systems Technology (PerDial 2019), Mar 2019, Kyoto, Japan. ⟨hal-02013174⟩